for paper only
agg_MAE$method[agg_MAE$method=="_best_Aalst"] = "TS (best)"
agg_MAE$method[agg_MAE$method=="adaptive_FA_zero_combined"] = "adaptive FA"
agg_MAE$method[agg_MAE$method=="mean_FA_zero_combined"] = "mean FA"
agg_MAE$method[agg_MAE$method=="predictive_FA_prefix_index"] = "predictive FA (multiple)"
agg_MAE$method[agg_MAE$method=="predictive_FA_zero_combined"] = "predictive FA (single)"
agg_MAE$method[agg_MAE$method=="simple_regression_prefix_index"] = "black box (multiple)"
agg_MAE$method[agg_MAE$method=="simple_regression_zero_combined"] = "black box (single)"
agg_MAE$method[agg_MAE$method=="_SPN_Rogge-Solti"] = "stochastic Petri net"
dat = data.frame()
for (dset in levels(agg_MAE$dataset)) {
df = agg_MAE[agg_MAE$dataset==dset,]
df$Order = rank(df$Avg_weighted_MAE_days,ties.method = "min")
dat = rbind(dat, df)
}
dat_ = ddply(dat, .(method), summarize, Mean=mean(Order))
foo = ddply(dat, .(method), summarize, std=sd(Order))
dat_ = merge(dat_,foo)
dat_ = dat_[order(dat_$Mean),]
pdf(file="mean-ranking.pdf",family="CM Roman",width=4,height=2)
p=ggplot(dat_, aes(x=reorder(method, -Mean), y=Mean, fill=method, group=method)) +
geom_bar(stat="identity") + theme(text=element_text(size=24))+
geom_errorbar(aes(ymin=Mean-std, ymax=Mean+std),width=.2) +
theme_bw() + coord_flip() + theme(legend.position="none") + ylab("mean ranking")+
theme(axis.title.y=element_blank(),axis.text=element_text(color="black"),axis.ticks.y=element_blank())+
scale_y_continuous(expand = c(0,0), limits=c(0,9),breaks = seq(1,7,2), labels =as.character(seq(1,7,2)))+
scale_fill_manual(values=cbPalette)
print(p)
dev.off()
null device
1
embed_fonts("mean-ranking.pdf",outfile="mean-ranking.pdf")
ggplotly(p)

dat$case_duration = 0
dat$case_duration[dat$dataset=="BPI2012A"] = 7.5
dat$case_duration[dat$dataset=="BPI2012O"] = 15.1
dat$case_duration[dat$dataset=="BPI2012W"] = 11.4
dat$case_duration[dat$dataset=="BPI2012W_no_dup"] = 11.4
dat$case_duration[dat$dataset=="CreditRequirement"] = 0.95
dat$case_duration[dat$dataset=="helpdesk"] = 7.3
dat$case_duration[dat$dataset=="minit_invoice_10"] = 2.225
dat$case_duration[dat$dataset=="traffic_fines_139"] = 582
dat$case_duration[dat$dataset=="hospital_billing_977"] = 165.2
dat$Avg_weighted_MAE_days = dat$Avg_weighted_MAE_days / dat$case_duration
dat$std = dat$std / dat$case_duration
dat$Order = NULL
dat_ = ddply(dat, .(method), summarize, Mean=mean(Avg_weighted_MAE_days))
foo = ddply(dat, .(method), summarize, std=sd(Avg_weighted_MAE_days))
dat_ = merge(dat_,foo)
pdf(file="mean-ranking2.pdf",family="CM Roman",width=4,height=2)
p=ggplot(dat_, aes(x=reorder(method, -Mean), y=Mean, fill=method, group=method)) +
geom_bar(stat="identity") + theme(text=element_text(size=24))+
geom_errorbar(aes(ymin=Mean-std, ymax=Mean+std),width=.2) +
theme_bw() + coord_flip() + theme(legend.position="none") + ylab("Average normalized MAE")+
theme(axis.title.y=element_blank(),axis.text=element_text(color="black"),axis.ticks.y=element_blank())+
scale_y_continuous(expand = c(0,0), limits=c(0,0.9), breaks = seq(0,0.8,0.2), labels =as.character(seq(0,0.8,0.2)))+
scale_fill_manual(values=cbPalette)
print(p)
dev.off()
null device
1
embed_fonts("mean-ranking2.pdf",outfile="mean-ranking2.pdf")
ggplotly(p)

dat_ = ddply(dat, .(method), summarize, Mean=mean(std))
foo = ddply(dat, .(method), summarize, Std=sd(std))
dat_ = merge(dat_,foo)
pdf(file="mean-ranking3.pdf",family="CM Roman",width=4,height=2)
p=ggplot(dat_, aes(x=reorder(method, -Mean), y=Mean, fill=method, group=method)) +
geom_bar(stat="identity") + theme(text=element_text(size=24))+
geom_errorbar(aes(ymin=Mean-Std, ymax=Mean+Std),width=.2) +
theme_bw() + coord_flip() + theme(legend.position="none") + ylab("Average normalized standard deviation")+
theme(axis.title.y=element_blank(),axis.text=element_text(color="black"),axis.ticks.y=element_blank())+
scale_y_continuous(expand = c(0,0), limits=c(-0.02,0.45), breaks = seq(0,0.4,0.1), labels =as.character(seq(0,0.4,0.1)))+
scale_fill_manual(values=cbPalette)
print(p)
dev.off()
null device
1
embed_fonts("mean-ranking3.pdf",outfile="mean-ranking3.pdf")
ggplotly(p)
